code: def linear_effect(
      df: pd.DataFrame, treatment_column: str, outcome_column: str
  ) -> float:
      """
      Computes the linear coefficient from regressing the outcome on the treatment: cov(outcome, treatment)/var(treatment)
  
      Parameters
      ----------
      df : Pandas' DataFrame
          A Pandas' DataFrame with target and prediction scores.
  
      treatment_column : str
          The name of the treatment column in `df`.
  
      outcome_column : str
          The name of the outcome column in `df`.
  
  
      Returns
      ----------
      effect: float
          The linear coefficient from regressing the outcome on the treatment: cov(outcome, treatment)/var(treatment)
      """
      return _apply_effect(
          linear_coefficient_evaluator,
          df,
          treatment_column,
          outcome_column,
      )
  
file_path: fklearn/src/fklearn/causal/effects.py
imports: import pandas
  from toolz import curry
  from typing import Dict, Callable
  from fklearn.validation.evaluators import spearman_evaluator, correlation_evaluator, linear_coefficient_evaluator, exponential_coefficient_evaluator, logistic_coefficient_evaluator
label_desc: Use image node_id 2 for calling a global function with example usage: linear_effect(df, treatment_column, outcome_column) and returns: _apply_effect